import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv1d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv1d(5, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
        self.bn = nn.BatchNorm2d(20)

    def forward(self, x):
        x = self.conv1(x)  # (13，10，12）
        x = F.max_pool1d(x, 2) # （13，10, 6）
        # print(x.size())
        x = F.relu(x) + F.relu(-x)  # （13，10，12，12）
        x = F.relu(F.max_pool1d(self.conv2_drop(self.conv2(x)), 2)) #（13，20，4，4）
        # x = self.bn(x)
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        # x = F.dropout(x, training=self.tr)
        x = self.fc2(x)
        return x


dummy_input = torch.rand(1, 13,  1, 16)
model = Net()
# y ={'aa':11}
output = model(dummy_input,)
with SummaryWriter(comment="learning_graph") as w:
    w.add_graph(model, (dummy_input,))
